Llama 3.2 11B Vision Instruct
11BMeta Llama 3.2
Multimodal Llama with image understanding. Vision encoder adds ~2GB VRAM overhead.
131K
Max Context
2
Quant Variants
GGUF Q8_0
Best Quality
99.5%
Accuracy Retained
Quantization Variants
Per-quant VRAM, quality loss, and inference speed on RTX 4090
Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports
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Running Llama 3.2 11B Vision Instruct locally
At Q4_K_M and 4K of context, Llama 3.2 11B Vision Instruct needs about 7.7 GB — 6.3 GB of weights, 0.63 GB of KV cache and a 0.7 GB activation buffer. The smallest cards in this index that clear that comfortably are 10 GB ones — 2 of them, from the Arc B570 10G up — and 67 of the 78 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.
What longer context costs
Going from 4K to 32K adds about 4.37 GB, taking the total to 12.5 GB. Weights do not move with context — only the KV cache does, and it grows linearly, so this is the number to watch when planning for long documents. The model's native window is 128K; holding all of it at Q4_K_M would need about 29 GB.
Which build to download
This index only tracks Llama 3.2 11B Vision Instruct in GGUF, across 2 levels (Q4_K_M, Q8_0). Q8_0 carries the lowest published perplexity loss at 0.5%. The fastest level listed is Q4_K_M at about 88 tok/s on an RTX 4090, batch 1 — an estimate, not a run on this site's hardware. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 — CUDA only, and now archived, so local use rather than serving.
Common questions
- How much VRAM does Llama 3.2 11B Vision Instruct need?
- About 7.7 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 12.5 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
- Will Llama 3.2 11B Vision Instruct run on a 10GB GPU?
- Yes — at Q4_K_M and 4K context it needs about 7.7 GB, which leaves 2.3 GB spare on a Arc B570 10G, or on any of the other 10 GB cards here (2 in all). That is the smallest size in this index that clears it comfortably; 67 of 78 do.
- Which quantization of Llama 3.2 11B Vision Instruct should I use?
- Q8_0 has the lowest published quality loss (0.5%), and Q4_K_M is the level most people run. All 2 levels in the index are Q4_K_M, Q8_0.
Where this model fits
Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.
- Runs comfortably on
- Arc B570 10G10GB · +2.3RTX 3080 10G10GB · +2.3RTX 3060 12G12GB · +4.3Radeon RX 6700 XT12GB · +4.3Radeon RX 7700 XT12GB · +4.3
- Tight but possible
- RTX 40608GBRTX 4060 Ti 8G8GBRadeon RX 9060 XT 8G8GB
- Just misses
- Mac M1 8G+1.7GB overMac M3 8G+1.7GB over
- Ships in
- GGUF
- Try it yourself
- Size it in the calculatorGenerate the run command
How to actually run this
Deployment guides for this model and this class of hardware.
This page's figures change when the model or the runtime does.Last updated 2026-10-20 RSS → /feed.xml